
In multi-task learning, most prior research focuses on allowing the agent to perform multiple tasks without considering the varying similarity between tasks.
The transfer of information between tasks is also not normally addressed explicitly.
In [1]}, the agent was trained to learn how to perform a series of tasks in a sequence, sharing some weights in earlier layers and with independently trained heads added for each task.
Yee et al. [2]} expanded on this by maintaining a shared policy between the independently trained and attempting to distil information from task specific experts.
Later research expanded this idea to hierarchical architectures.
This allowed the agent to perform multiple sub-tasks as a part of a longer, more complex task.
In the work of Shu et al.[3]}, the multi-level hierarchical policy decomposes a complex task into multiple levels of sub-task, with each having a human instruction as a reference.
